SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS

By Alistair Kennedy, Diana Inkpen. International Conference on Climate Informatics, 2006

823 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e14609a3a6c6f8ef3269d3e0728f88da57826698

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In Artificial Intelligence

Citations in the corpus

A review of natural language processing techniques for opinion mining systems (2017, methodology): “Intensification and diminution are increasing and decreasing the strength of sentiment, respectively, which are also useful for opinion mining [40, 65, 41, 66].”
Opinion Mining and Sentiment Analysis (2008, background): “Further improvement probably needs deeper (syntactic) analysis of the sentence [152].”
Text Mining in Big Data Analytics (2020, background): “Kennedy and Inkpen [40] and Tripathy et al.”
Interpreting Neural Networks to Improve Politeness Comprehension (2016, methodology): “Substantial previous work has employed machine learning models for other sentiment analysis style tasks (Pang et al., 2002; Pang and Lee, 2004; Kennedy and Inkpen, 2006; Go et al., 2009; Ghiassi et al., 2013).”
Sentiment Analysis: An Overview from Linguistics (2016, positive): “Much of the work has focused on analyzing reviews of movies, books and consumer products (Dave et al 2003, Hu & Liu 2004, Kennedy & Inkpen 2006, Turney 2002).”
The predictive power of public Twitter sentiment for forecasting cryptocurrency prices (2020, result): “This is consistent with the results of Kennedy and Inkpen (2006), who observe that lexicon-based approaches generally have a positive bias, which can be attributed to a human tendency to prefer positive language.”
Sentiment Analysis of Short Informal Texts (2014, background): “However, several studies have pointed out the inadequacy of this solution (Kennedy & Inkpen, 2006; Taboada, Brooke, Toﬁloski, Voll, & Stede, 2011).”
Learning Word Vectors for Sentiment Analysis (2011, neutral): “00 Contextual Valence Shifters (Kennedy and Inkpen, 2006) 86.”
Affective News: The Automated Coding of Sentiment in Political Texts (2012, background): “Few researchers use the dictionary without encountering the need to generate expanded and/or disambiguated versions (e.g., Hogenraad et al., 2003; Kennedy & Inkpen, 2006; Pennebaker et al., 2003; Scharl, Pollach, & Bauer, 2003).10 Rather than removing ambiguity, some seek to preserve it by adopting…”
Co-Training for Cross-Lingual Sentiment Classification (2009, background): “Kennedy and Inkpen (2006) determine the sentiment of a customer review by counting positive and negative terms and taking into account contextual valence shifters, such as negations and intensifiers.”
Joint sentiment/topic model for sentiment analysis (2009, methodology): “In addition, the previous reported results [15, 24, 10] were all based on 10-fold cross validation in a test set comprising of 200 documents only 9 , our experimental results reported here are based on the whole movie review dataset with a total of 2000 documents.”
A survey on sentiment detection of reviews (2009, methodology): “In fact, most of the literatures showed that SVM and Naı̈ve Bayes are perfect methods in single domain document sentiment classification (Aue and Gamon, 2005; Beineke et al., 2004; Kennedy and Inkpen, 2006; Lin et al., 2006; Matsumoto et al., 2005; Mullen and Collier, 2004; Pang and Lee, 2004; Pang ”
Compositional Matrix-Space Models for Sentiment Analysis (2011, background): “(2005) , Kennedy and Inkpen (2006), Shaikh et al.”
Sentiment Analysis and Subjectivity (2010, background): “Sentiment classification is perhaps the most widely studied topic [3, 6, 8, 12, 13, 15, 16, 18, 27, 28, 33, 34, 35, 44, 45, 62, 64, 66, 67, 68, 70, 71, 73, 79, 80, 86, 92, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 111].”
Lexicon-Based Methods for Sentiment Analysis (2011, methodology): “We have not presented all the combinations of features, but we know from other experiments, that, for instance, basic negation is more important than basic intensiﬁcation, as also shown by Kennedy and Inkpen (2006).”